The increasing demands for high accuracy in mechatronic systems necessitate the incorporation of parameter variations in feedforward control. The aim of this paper is to develop a data-driven approach for direct learning of parameter-varying feedforward control to increase tracking performance. The developed approach is based on kernel-regularized function estimation in conjunction with iterative learning to directly learn parameter-varying feedforward control from data. This approach enables high tracking performance for feedforward control of linear parameter-varying dynamics, providing flexibility to varying reference tasks. The developed framework is validated on a benchmark industrial experimental setup featuring a belt-driven carriage.
@article{arxiv.2502.21105,
title = {Parameter-Varying Feedforward Control: A Kernel-Based Learning Approach},
author = {Max van Haren and Lennart Blanken and Tom Oomen},
journal= {arXiv preprint arXiv:2502.21105},
year = {2025}
}